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Optimization Design of the Multi-Layer Cross-Sectional Layout of An Umbilical Based on the GA-GLM 被引量:1
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作者 YANG Zhi-xun YIN Xu +5 位作者 FAN Zhi-rui YAN Jun LU Yu-cheng SU Qi MAO Yandong WANG Hua-lin 《China Ocean Engineering》 SCIE EI CSCD 2024年第2期247-254,共8页
Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components direct... Marine umbilical is one of the key equipment for subsea oil and gas exploitation,which is usually integrated by a great number of different functional components with multi-layers.The layout of these components directly affects manufacturing,operation and storage performances of the umbilical.For the multi-layer cross-sectional layout design of the umbilical,a quantifiable multi-objective optimization model is established according to the operation and storage requirements.Considering the manufacturing factors,the multi-layering strategy based on contact point identification is introduced for a great number of functional components.Then,the GA-GLM global optimization algorithm is proposed combining the genetic algorithm and the generalized multiplier method,and the selection operator of the genetic algorithm is improved based on the steepest descent method.Genetic algorithm is used to find the optimal solution in the global space,which can converge from any initial layout to the feasible layout solution.The feasible layout solution is taken as the initial value of the generalized multiplier method for fast and accurate solution.Finally,taking umbilicals with a great number of components as examples,the results show that the cross-sectional performance of the umbilical obtained by optimization algorithm is better and the solution efficiency is higher.Meanwhile,the multi-layering strategy is effective and feasible.The design method proposed in this paper can quickly obtain the optimal multi-layer cross-sectional layout,which replaces the manual design,and provides useful reference and guidance for the umbilical industry. 展开更多
关键词 UMBILICAL cross-sectional layout MULTI-LAYERS GA-GLM optimization
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UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach 被引量:1
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作者 Jiawen Kang Junlong Chen +6 位作者 Minrui Xu Zehui Xiong Yutao Jiao Luchao Han Dusit Niyato Yongju Tong Shengli Xie 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第2期430-445,共16页
Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metavers... Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses. 展开更多
关键词 AVATAR blockchain metaverses multi-agent deep reinforcement learning transformer UAVS
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Cognitive interference decision method for air defense missile fuze based on reinforcement learning 被引量:1
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作者 Dingkun Huang Xiaopeng Yan +2 位作者 Jian Dai Xinwei Wang Yangtian Liu 《Defence Technology(防务技术)》 SCIE EI CAS CSCD 2024年第2期393-404,共12页
To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-lea... To solve the problem of the low interference success rate of air defense missile radio fuzes due to the unified interference form of the traditional fuze interference system,an interference decision method based Q-learning algorithm is proposed.First,dividing the distance between the missile and the target into multiple states to increase the quantity of state spaces.Second,a multidimensional motion space is utilized,and the search range of which changes with the distance of the projectile,to select parameters and minimize the amount of ineffective interference parameters.The interference effect is determined by detecting whether the fuze signal disappears.Finally,a weighted reward function is used to determine the reward value based on the range state,output power,and parameter quantity information of the interference form.The effectiveness of the proposed method in selecting the range of motion space parameters and designing the discrimination degree of the reward function has been verified through offline experiments involving full-range missile rendezvous.The optimal interference form for each distance state has been obtained.Compared with the single-interference decision method,the proposed decision method can effectively improve the success rate of interference. 展开更多
关键词 Cognitive radio Interference decision Radio fuze reinforcement learning Interference strategy optimization
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Association between triglyceride-glucose index and colorectal polyps:A retrospective cross-sectional study 被引量:1
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作者 Ya-Jie Teng Ying-Xue Yang +5 位作者 Jing-Jing Yang Qiu-Yan Lu Jia-Yi Shi Jian-Hao Xu Jie Bao Qing-Hua Wang 《World Journal of Gastrointestinal Endoscopy》 2024年第2期55-63,共9页
BACKGROUND Colorectal polyps(CPs)are frequently occurring abnormal growths in the colorectum,and are a primary precursor of colorectal cancer(CRC).The triglyceride-glucose(TyG)index is a novel marker that assesses met... BACKGROUND Colorectal polyps(CPs)are frequently occurring abnormal growths in the colorectum,and are a primary precursor of colorectal cancer(CRC).The triglyceride-glucose(TyG)index is a novel marker that assesses metabolic health and insulin resistance,and has been linked to gastrointestinal cancers.AIM To investigate the potential association between the TyG index and CPs,as the relation between them has not been documented.METHODS A total of 2537 persons undergoing a routine health physical examination and colonoscopy at The First People's Hospital of Kunshan,Jiangsu Province,China,between January 2020 and December 2022 were included in this retrospective cross-sectional study.After excluding individuals who did not meet the eligibility criteria,descriptive statistics were used to compare characteristics between patients with and without CPs.Logistic regression analyses were conducted to determine the associations between the TyG index and the prevalence of CPs.The TyG index was calculated using the following formula:Ln[triglyceride(mg/dL)×glucose(mg/dL)/2].The presence and types of CPs was determined based on data from colonoscopy reports and pathology reports.RESULTS A nonlinear relation between the TyG index and the prevalence of CPs was identified,and exhibited a curvilinear pattern with a cut-off point of 2.31.A significant association was observed before the turning point,with an odds ratio(95% confidence interval)of 1.70(1.40,2.06),P<0.0001.However,the association between the TyG index and CPs was not significant after the cut-off point,with an odds ratio(95% confidence interval)of 0.57(0.27,1.23),P=0.1521.CONCLUSION Our study revealed a curvilinear association between the TyG index and CPs in Chinese individuals,suggesting its potential utility in developing colonoscopy screening strategies for preventing CRC. 展开更多
关键词 Triglyceride-glucose index Colorectal polyps Colorectal cancer Insulin resistance cross-sectional study
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Mental health and insomnia problems in healthcare workers after the COVID-19 pandemic:A multicenter cross-sectional study 被引量:1
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作者 Wei Ding Min-Zhong Wang +8 位作者 Xian-Wei Zeng Zhen-Hua Liu Yao Meng Hui-Ting Hu Yuan Zhang Yu-Guang Guan Fan-Gang Meng Jian-Guo Zhang Shu Wang 《World Journal of Psychiatry》 SCIE 2024年第5期704-714,共11页
BACKGROUND Healthcare workers(HCWs)are at increased risk of contracting coronavirus disease 2019(COVID-19)as well as worsening mental health problems and insomnia.These problems can persist for a long period,even afte... BACKGROUND Healthcare workers(HCWs)are at increased risk of contracting coronavirus disease 2019(COVID-19)as well as worsening mental health problems and insomnia.These problems can persist for a long period,even after the pandemic.However,less is known about this topic.AIM To analyze mental health,insomnia problems,and their influencing factors in HCWs after the COVID-19 pandemic.METHODS This multicenter cross-sectional,hospital-based study was conducted from June 1,2023 to June 30,2023,which was a half-year after the end of the COVID-19 emergency.Region-stratified population-based cluster sampling was applied at the provincial level for Chinese HCWs.Symptoms such as anxiety,depression,and insomnia were evaluated by the Generalized Anxiety Disorder-7,Patient Health Questionnaire-9,and Insomnia Severity Index.Factors influencing the symptoms were identified by multivariable logistic regression.RESULTS A total of 2000 participants were invited,for a response rate of 70.6%.A total of 1412 HCWs[618(43.8%)doctors,583(41.3%)nurses and 211(14.9%)nonfrontline],254(18.0%),231(16.4%),and 289(20.5%)had symptoms of anxiety,depression,and insomnia,respectively;severe symptoms were found in 58(4.1%),49(3.5%),and 111(7.9%)of the participants.Nurses,female sex,and hospitalization for COVID-19 were risk factors for anxiety,depression,and insomnia symptoms;moreover,death from family or friends was a risk factor for insomnia symptoms.During the COVID-19 outbreak,most[1086(76.9%)]of the participating HCWs received psychological interventions,while nearly all[994(70.4%)]of them had received public psychological education.Only 102(7.2%)of the HCWs received individual counseling from COVID-19.CONCLUSION Although the mental health and sleep problems of HCWs were relieved after the COVID-19 pandemic,they still faced challenges and greater risks than did the general population.Identifying risk factors would help in providing targeted interventions.In addition,although a major proportion of HCWs have received public psychological education,individual interventions are still insufficient. 展开更多
关键词 COVID-19 Mental health Psychological symptoms INSOMNIA Sleep disorders cross-sectional study Epidemiological study
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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications 被引量:4
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作者 Ding Wang Ning Gao +2 位作者 Derong Liu Jinna Li Frank L.Lewis 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第1期18-36,共19页
Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and ... Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence. 展开更多
关键词 Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)
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Association between Residential Greenness and Cardiometabolic Risk Factors among Adults in Rural Xinjiang Uygur Autonomous Region,China:A Cross-Sectional Study
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作者 Leyao Jian Bo Yang +10 位作者 Rulin Ma Shuxia Guo Jia He Yu Li Yusong Ding Dongsheng Rui Yidan Mao Xin He Xueying Sun Shengyu Liao Heng Guo 《Biomedical and Environmental Sciences》 SCIE CAS CSCD 2024年第10期1184-1194,共11页
Objective This study aimed to explore the relationships between residential greenness and cardiometabolic risk factors among rural adults in Xinjiang Uygur Autonomous Region(Xinjiang)and thus provide a theoretical bas... Objective This study aimed to explore the relationships between residential greenness and cardiometabolic risk factors among rural adults in Xinjiang Uygur Autonomous Region(Xinjiang)and thus provide a theoretical basis and data support for improving the health of residents in this region.Methods We recruited 9,723 adult rural residents from the 51st Regiment of the Third Division of the Xinjiang Production and Construction Corps in September 2016.The normalized difference vegetation index(NDVI)was used to estimate residential greenness.The generalized linear mixed model(GLMM)was used to examine the association between residential greenness and cardiometabolic risk factors.Results Higher residential greenness was associated with lower cardiometabolic risk factor prevalence.After adjustments were made for age,sex,education,and marital status,for each interquartile range(IQR)increase of NDVI500-m,the risk of hypertension was reduced by 10.3%(OR=0.897,95%CI=0.836-0.962),the risk of obesity by 20.5%(OR=0.795,95%CI=0.695-0.910),the risk of type 2 diabetes by 15.1%(OR=0.849,95%CI=0.740-0.974),and the risk of dyslipidemia by 10.5%(OR=0.895,95%CI=0.825-0.971).Risk factor aggregation was reduced by 20.4%(OR=0.796,95%CI=0.716-0.885)for the same.Stratified analysis showed that NDVI500-m was associated more strongly with hypertension,dyslipidemia,and risk factor aggregation among male participants.The association of NDVI500-m with type 2 diabetes was stronger among participants with a higher education level.PM10 and physical activity mediated 1.9%-9.2%of the associations between NDVI500-m and obesity,dyslipidemia,and risk factor aggregation.Conclusion Higher residential greenness has a protective effect against cardiometabolic risk factors among rural residents in Xinjiang.Increasing the area of green space around residences is an effective measure to reduce the burden of cardiometabolic-related diseases among rural residents in Xinjiang. 展开更多
关键词 Green space Cardiometabolic risk factors cross-sectional study Rural adults
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Bridge Bidding via Deep Reinforcement Learning and Belief Monte Carlo Search
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作者 Zizhang Qiu Shouguang Wang +1 位作者 Dan You MengChu Zhou 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第10期2111-2122,共12页
Contract Bridge,a four-player imperfect information game,comprises two phases:bidding and playing.While computer programs excel at playing,bidding presents a challenging aspect due to the need for information exchange... Contract Bridge,a four-player imperfect information game,comprises two phases:bidding and playing.While computer programs excel at playing,bidding presents a challenging aspect due to the need for information exchange with partners and interference with communication of opponents.In this work,we introduce a Bridge bidding agent that combines supervised learning,deep reinforcement learning via self-play,and a test-time search approach.Our experiments demonstrate that our agent outperforms WBridge5,a highly regarded computer Bridge software that has won multiple world championships,by a performance of 0.98 IMPs(international match points)per deal over 10000 deals,with a much cost-effective approach.The performance significantly surpasses previous state-of-the-art(0.85 IMPs per deal).Note 0.1 IMPs per deal is a significant improvement in Bridge bidding. 展开更多
关键词 Contract Bridge reinforcement learning SEARCH
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Distributed Graph Database Load Balancing Method Based on Deep Reinforcement Learning
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作者 Shuming Sha Naiwang Guo +1 位作者 Wang Luo Yong Zhang 《Computers, Materials & Continua》 SCIE EI 2024年第6期5105-5124,共20页
This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies.Unlike indepen-dent batch tasks,workflows typically consist of multiple subtasks with intrinsic correlations and dependenci... This paper focuses on the scheduling problem of workflow tasks that exhibit interdependencies.Unlike indepen-dent batch tasks,workflows typically consist of multiple subtasks with intrinsic correlations and dependencies.It necessitates the distribution of various computational tasks to appropriate computing node resources in accor-dance with task dependencies to ensure the smooth completion of the entire workflow.Workflow scheduling must consider an array of factors,including task dependencies,availability of computational resources,and the schedulability of tasks.Therefore,this paper delves into the distributed graph database workflow task scheduling problem and proposes a workflow scheduling methodology based on deep reinforcement learning(DRL).The method optimizes the maximum completion time(makespan)and response time of workflow tasks,aiming to enhance the responsiveness of workflow tasks while ensuring the minimization of the makespan.The experimental results indicate that the Q-learning Deep Reinforcement Learning(Q-DRL)algorithm markedly diminishes the makespan and refines the average response time within distributed graph database environments.In quantifying makespan,Q-DRL achieves mean reductions of 12.4%and 11.9%over established First-fit and Random scheduling strategies,respectively.Additionally,Q-DRL surpasses the performance of both DRL-Cloud and Improved Deep Q-learning Network(IDQN)algorithms,with improvements standing at 4.4%and 2.6%,respectively.With reference to average response time,the Q-DRL approach exhibits a significantly enhanced performance in the scheduling of workflow tasks,decreasing the average by 2.27%and 4.71%when compared to IDQN and DRL-Cloud,respectively.The Q-DRL algorithm also demonstrates a notable increase in the efficiency of system resource utilization,reducing the average idle rate by 5.02%and 9.30%in comparison to IDQN and DRL-Cloud,respectively.These findings support the assertion that Q-DRL not only upholds a lower average idle rate but also effectively curtails the average response time,thereby substantially improving processing efficiency and optimizing resource utilization within distributed graph database systems. 展开更多
关键词 reinforcement learning WORKFLOW task scheduling load balancing
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Nano-scale Reinforcements and Properties of Al-Si-Cu Alloy Processed by High-Pressure Torsion
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作者 DONG Ying WU Siyuan +4 位作者 HE Ziyang LIANG Chen CHENG Feng HE Zuwei QIAN Chenhao 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS CSCD 2024年第5期1253-1259,共7页
To improve the comprehensive mechanical properties of Al-Si-Cu alloy,it was treated by a high-pressure torsion process,and the effect of the deformation degree on the microstructure and properties of the Al-Si-Cu allo... To improve the comprehensive mechanical properties of Al-Si-Cu alloy,it was treated by a high-pressure torsion process,and the effect of the deformation degree on the microstructure and properties of the Al-Si-Cu alloy was studied.The results show that the reinforcements(β-Si andθ-CuAl_(2)phases)of the Al-Si-Cu alloy are dispersed in theα-Al matrix phase with finer phase size after the treatment.The processed samples exhibit grain sizes in the submicron or even nanometer range,which effectively improves the mechanical properties of the material.The hardness and strength of the deformed alloy are both significantly raised to 268 HV and 390.04 MPa by 10 turns HPT process,and the fracture morphology shows that the material gradually transits from brittle to plastic before and after deformation.The elements interdiffusion at the interface between the phases has also been effectively enhanced.In addition,it is found that the severe plastic deformation at room temperature induces a ternary eutectic reaction,resulting in the formation of ternary Al+Si+CuAl_(2)eutectic. 展开更多
关键词 Al-Si-Cu alloy high-pressure torsion nano-scale reinforcements ternary eutectic
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QoS Routing Optimization Based on Deep Reinforcement Learning in SDN
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作者 Yu Song Xusheng Qian +2 位作者 Nan Zhang Wei Wang Ao Xiong 《Computers, Materials & Continua》 SCIE EI 2024年第5期3007-3021,共15页
To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront thechallenge of managing the surging demand for data traffic. Within this realm, the network imposes stringentQu... To enhance the efficiency and expediency of issuing e-licenses within the power sector, we must confront thechallenge of managing the surging demand for data traffic. Within this realm, the network imposes stringentQuality of Service (QoS) requirements, revealing the inadequacies of traditional routing allocation mechanismsin accommodating such extensive data flows. In response to the imperative of handling a substantial influx of datarequests promptly and alleviating the constraints of existing technologies and network congestion, we present anarchitecture forQoS routing optimizationwith in SoftwareDefinedNetwork (SDN), leveraging deep reinforcementlearning. This innovative approach entails the separation of SDN control and transmission functionalities, centralizingcontrol over data forwardingwhile integrating deep reinforcement learning for informed routing decisions. Byfactoring in considerations such as delay, bandwidth, jitter rate, and packet loss rate, we design a reward function toguide theDeepDeterministic PolicyGradient (DDPG) algorithmin learning the optimal routing strategy to furnishsuperior QoS provision. In our empirical investigations, we juxtapose the performance of Deep ReinforcementLearning (DRL) against that of Shortest Path (SP) algorithms in terms of data packet transmission delay. Theexperimental simulation results show that our proposed algorithm has significant efficacy in reducing networkdelay and improving the overall transmission efficiency, which is superior to the traditional methods. 展开更多
关键词 Deep reinforcement learning SDN route optimization QOS
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Role Dynamic Allocation of Human-Robot Cooperation Based on Reinforcement Learning in an Installation of Curtain Wall
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作者 Zhiguang Liu Shilin Wang +2 位作者 Jian Zhao Jianhong Hao Fei Yu 《Computer Modeling in Engineering & Sciences》 SCIE EI 2024年第1期473-487,共15页
A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve humanrobot cooperation performance for a curtain wall installation task.This method breaks the traditional idea that ... A real-time adaptive roles allocation method based on reinforcement learning is proposed to improve humanrobot cooperation performance for a curtain wall installation task.This method breaks the traditional idea that the robot is regarded as the follower or only adjusts the leader and the follower in cooperation.In this paper,a self-learning method is proposed which can dynamically adapt and continuously adjust the initiative weight of the robot according to the change of the task.Firstly,the physical human-robot cooperation model,including the role factor is built.Then,a reinforcement learningmodel that can adjust the role factor in real time is established,and a reward and actionmodel is designed.The role factor can be adjusted continuously according to the comprehensive performance of the human-robot interaction force and the robot’s Jerk during the repeated installation.Finally,the roles adjustment rule established above continuously improves the comprehensive performance.Experiments of the dynamic roles allocation and the effect of the performance weighting coefficient on the result have been verified.The results show that the proposed method can realize the role adaptation and achieve the dual optimization goal of reducing the sum of the cooperator force and the robot’s Jerk. 展开更多
关键词 Human-robot cooperation roles allocation reinforcement learning
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Reinforcement Learning in Process Industries:Review and Perspective
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作者 Oguzhan Dogru Junyao Xie +6 位作者 Om Prakash Ranjith Chiplunkar Jansen Soesanto Hongtian Chen Kirubakaran Velswamy Fadi Ibrahim Biao Huang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2024年第2期283-300,共18页
This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control ... This survey paper provides a review and perspective on intermediate and advanced reinforcement learning(RL)techniques in process industries. It offers a holistic approach by covering all levels of the process control hierarchy. The survey paper presents a comprehensive overview of RL algorithms,including fundamental concepts like Markov decision processes and different approaches to RL, such as value-based, policy-based, and actor-critic methods, while also discussing the relationship between classical control and RL. It further reviews the wide-ranging applications of RL in process industries, such as soft sensors, low-level control, high-level control, distributed process control, fault detection and fault tolerant control, optimization,planning, scheduling, and supply chain. The survey paper discusses the limitations and advantages, trends and new applications, and opportunities and future prospects for RL in process industries. Moreover, it highlights the need for a holistic approach in complex systems due to the growing importance of digitalization in the process industries. 展开更多
关键词 Process control process systems engineering reinforcement learning
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Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees
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作者 Xiangkun He Wenhui Huang Chen Lv 《Engineering》 SCIE EI CAS CSCD 2024年第2期77-89,共13页
While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present... While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies. 展开更多
关键词 Autonomous vehicle DECISION-MAKING reinforcement learning Adversarial attack Safety guarantee
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Reinforcement learning based edge computing in B5G
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作者 Jiachen Yang Yiwen Sun +4 位作者 Yutian Lei Zhuo Zhang Yang Li Yongjun Bao Zhihan Lv 《Digital Communications and Networks》 SCIE CSCD 2024年第1期1-6,共6页
The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology promoter.At the same time,Beyond 5G will become one of the important supports f... The development of communication technology will promote the application of Internet of Things,and Beyond 5G will become a new technology promoter.At the same time,Beyond 5G will become one of the important supports for the development of edge computing technology.This paper proposes a communication task allocation algorithm based on deep reinforcement learning for vehicle-to-pedestrian communication scenarios in edge computing.Through trial and error learning of agent,the optimal spectrum and power can be determined for transmission without global information,so as to balance the communication between vehicle-to-pedestrian and vehicle-to-infrastructure.The results show that the agent can effectively improve vehicle-to-infrastructure communication rate as well as meeting the delay constraints on the vehicle-to-pedestrian link. 展开更多
关键词 reinforcement learning Edge computing Beyond 5G Vehicle-to-pedestrian
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Combining reinforcement learning with mathematical programming:An approach for optimal design of heat exchanger networks
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作者 Hui Tan Xiaodong Hong +4 位作者 Zuwei Liao Jingyuan Sun Yao Yang Jingdai Wang Yongrong Yang 《Chinese Journal of Chemical Engineering》 SCIE EI CAS CSCD 2024年第5期63-71,共9页
Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinea... Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales. 展开更多
关键词 Heat exchanger network reinforcement learning Mathematical programming Process design
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Task Offloading and Resource Allocation in NOMA-VEC:A Multi-Agent Deep Graph Reinforcement Learning Algorithm
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作者 Hu Yonghui Jin Zuodong +1 位作者 Qi Peng Tao Dan 《China Communications》 SCIE CSCD 2024年第8期79-88,共10页
Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in im... Vehicular edge computing(VEC)is emerging as a promising solution paradigm to meet the requirements of compute-intensive applications in internet of vehicle(IoV).Non-orthogonal multiple access(NOMA)has advantages in improving spectrum efficiency and dealing with bandwidth scarcity and cost.It is an encouraging progress combining VEC and NOMA.In this paper,we jointly optimize task offloading decision and resource allocation to maximize the service utility of the NOMA-VEC system.To solve the optimization problem,we propose a multiagent deep graph reinforcement learning algorithm.The algorithm extracts the topological features and relationship information between agents from the system state as observations,outputs task offloading decision and resource allocation simultaneously with local policy network,which is updated by a local learner.Simulation results demonstrate that the proposed method achieves a 1.52%∼5.80%improvement compared with the benchmark algorithms in system service utility. 展开更多
关键词 edge computing graph convolutional network reinforcement learning task offloading
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Deep Reinforcement Learning Based Joint Cooperation Clustering and Downlink Power Control for Cell-Free Massive MIMO
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作者 Du Mingjun Sun Xinghua +2 位作者 Zhang Yue Wang Junyuan Liu Pei 《China Communications》 SCIE CSCD 2024年第11期1-14,共14页
In recent times,various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multipleinput multiple-output(CF-mMIMO)networks.With the emergence of deep reinfo... In recent times,various power control and clustering approaches have been proposed to enhance overall performance for cell-free massive multipleinput multiple-output(CF-mMIMO)networks.With the emergence of deep reinforcement learning(DRL),significant progress has been made in the field of network optimization as DRL holds great promise for improving network performance and efficiency.In this work,our focus delves into the intricate challenge of joint cooperation clustering and downlink power control within CF-mMIMO networks.Leveraging the potent deep deterministic policy gradient(DDPG)algorithm,our objective is to maximize the proportional fairness(PF)for user rates,thereby aiming to achieve optimal network performance and resource utilization.Moreover,we harness the concept of“divide and conquer”strategy,introducing two innovative methods termed alternating DDPG(A-DDPG)and hierarchical DDPG(H-DDPG).These approaches aim to decompose the intricate joint optimization problem into more manageable sub-problems,thereby facilitating a more efficient resolution process.Our findings unequivo-cally showcase the superior efficacy of our proposed DDPG approach over the baseline schemes in both clustering and downlink power control.Furthermore,the A-DDPG and H-DDPG obtain higher performance gain than DDPG with lower computational complexity. 展开更多
关键词 cell-free massive MIMO CLUSTERING deep reinforcement learning power control
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Knowledge Reasoning Method Based on Deep Transfer Reinforcement Learning:DTRLpath
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作者 Shiming Lin Ling Ye +4 位作者 Yijie Zhuang Lingyun Lu Shaoqiu Zheng Chenxi Huang Ng Yin Kwee 《Computers, Materials & Continua》 SCIE EI 2024年第7期299-317,共19页
In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring mi... In recent years,with the continuous development of deep learning and knowledge graph reasoning methods,more and more researchers have shown great interest in improving knowledge graph reasoning methods by inferring missing facts through reasoning.By searching paths on the knowledge graph and making fact and link predictions based on these paths,deep learning-based Reinforcement Learning(RL)agents can demonstrate good performance and interpretability.Therefore,deep reinforcement learning-based knowledge reasoning methods have rapidly emerged in recent years and have become a hot research topic.However,even in a small and fixed knowledge graph reasoning action space,there are still a large number of invalid actions.It often leads to the interruption of RL agents’wandering due to the selection of invalid actions,resulting in a significant decrease in the success rate of path mining.In order to improve the success rate of RL agents in the early stages of path search,this article proposes a knowledge reasoning method based on Deep Transfer Reinforcement Learning path(DTRLpath).Before supervised pre-training and retraining,a pre-task of searching for effective actions in a single step is added.The RL agent is first trained in the pre-task to improve its ability to search for effective actions.Then,the trained agent is transferred to the target reasoning task for path search training,which improves its success rate in searching for target task paths.Finally,based on the comparative experimental results on the FB15K-237 and NELL-995 datasets,it can be concluded that the proposed method significantly improves the success rate of path search and outperforms similar methods in most reasoning tasks. 展开更多
关键词 Intelligent agent knowledge graph reasoning reinforcement transfer learning
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Enhancing Image Description Generation through Deep Reinforcement Learning:Fusing Multiple Visual Features and Reward Mechanisms
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作者 Yan Li Qiyuan Wang Kaidi Jia 《Computers, Materials & Continua》 SCIE EI 2024年第2期2469-2489,共21页
Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually imp... Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually impaired.This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images.Our method focuses on refining the reward function in deep reinforcement learning,facilitating the generation of precise descriptions by aligning visual and textual features more closely.Our approach comprises three key architectures.Firstly,it utilizes Residual Network 101(ResNet-101)and Faster Region-based Convolutional Neural Network(Faster R-CNN)to extract average and local image features,respectively,followed by the implementation of a dual attention mechanism for intricate feature fusion.Secondly,the Transformer model is engaged to derive contextual semantic features from textual data.Finally,the generation of descriptive text is executed through a two-layer long short-term memory network(LSTM),directed by the value and reward functions.Compared with the image description method that relies on deep learning,the score of Bilingual Evaluation Understudy(BLEU-1)is 0.762,which is 1.6%higher,and the score of BLEU-4 is 0.299.Consensus-based Image Description Evaluation(CIDEr)scored 0.998,Recall-Oriented Understudy for Gisting Evaluation(ROUGE)scored 0.552,the latter improved by 0.36%.These results not only attest to the viability of our approach but also highlight its superiority in the realm of image description.Future research can explore the integration of our method with other artificial intelligence(AI)domains,such as emotional AI,to create more nuanced and context-aware systems. 展开更多
关键词 Image description deep reinforcement learning attention mechanism
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